Abstract：
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint\'\' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
摘要：
大脑的功能连接（FC）图已被广泛认为是“指纹”，可用于识别一组受试者中的个体。研究表明，可以通过消除个人之间共享信息的影响来提高个人身份识别的准确性。然而,当前的研究不仅从FC图中提取了主体间的共享信息，还提取了个体特定的信息，导致个人之间共享信息和指纹信息的不完全分离，导致所有功能磁共振成像（fMRI）状态会话对的个体识别准确性较低，认知行为预测性能较差。在本文中,我们提出了一种结合条件变分自编码器（CVAE）网络和稀疏字典学习（SDL）模块来增强主体间变异性的方法。通过在编码和解码过程中嵌入fMRI状态信息，CVAE网络可以更好地捕获和代表个体之间的共同特征,并通过残差增强受试者间的变异性.我们对HumanConnectomeProject（HCP）数据的实验结果表明，通过使用CVAE和SDL获得的精细连接组可以准确地将个体与其余参与者区分开来。在会话对rest1-rest2和反向rest2-rest1中，成功精度分别达到99.7％和99.6％。在同一天进行的涉及任务-任务组合的识别实验中，识别准确率从94.2%到98.8%不等。此外，我们发现,前顶网络和默认网络对个体识别做出了最显著的贡献,并且在前顶网络和默认网络内部和之间发现了对个体识别做出显著贡献的边缘.此外,高级认知行为也可以用获得的精细连接体更好地预测，这表明更高的指纹可以用于导致更高的行为关联。总之,我们提出的框架为使用功能连接网络研究认知和行为提供了一种有前途的方法，促进对大脑功能的更深入理解。
